System and Methods for Standardizing Scoring of Individual Social Media Content
The disclosed embodiments provide systems and methods analyzing social media content using artificial intelligence/machine learning algorithms. In certain embodiments, the system collects social media data from one or more third-party social media networks associated with the user, where the social media data is comprised of two or more of post reactions, post comments, posting frequency, profile picture, public posting setting, grammar, and predetermined keywords. The system then analyzes, using a machine learning algorithm, the social media data of the user to calculate a social impact score for the user, and transmits the social impact score to the user. In some embodiments, the social impact score is calculated relative to other social impact scores.
This application claims the benefit of U.S. Prov. App. Nos. 63/152,889, 63/152,892, and 63/152,904, each of which is hereby incorporated in its entirety by reference.
FIELD OF THE INVENTIONThe present invention relates to methods, apparatus, and systems, including computer programs encoded on a computer storage medium, for collecting and analyzing social media posts across multiple social media platforms to address possible harmful posts.
BACKGROUND OF THE INVENTIONArtificial intelligence (AI) is the name of a field of research and techniques in which the goal is to create intelligent systems. Machine learning (ML) is an approach to achieve this goal. Deep learning (DL) is the set of latest most advanced techniques in ML.
The execution of machine learning models and artificial intelligence applications can be very resource intensive as large amounts of processing and storage resources can be consumed. The execution of such models and applications can be resource intensive, in part, because of the large amount of data that is fed into such machine learning models and artificial intelligence applications.
Current tools used in social media involve word-matching, which looks for the occurrence of the query words in social media posts. This type of search is not efficient because the presence or absence of words of the query compared to the quantity of social media does not necessarily confirm the relevance or irrelevance of the found documents. For example, a word search might find documents that contain words but that are contextually irrelevant. Or, if the user applied a different terminology for the query that is contextually or even texturally different than the one in the documents, the word-matching process would fail to match and locate relevant text.
Current word and image analysis are limited in their capabilities. For example, with word-matching research tools, it is crucial to create a word limit in the query presented to the system. Furthermore, all of the words should be in without extraneous detail. However, if the input includes too many generic words, the research tool will return irrelevant social media posts that contain these generic words. This task of choosing very few, but informative words, is challenging, and the user needs prior knowledge of the field to complete the task. The user should know what information is significant or insignificant and therefore, should or should not be included in the search (i.e., contextualization), and further, the proper/accepted terminology that is best for expressing the information (i.e., lexicographical textualization). If the user fails to include the important or correct terms or includes too many irrelevant details, the searching system will not operate successfully.
Even improved analytic tools face the same challenge that word-matching research tools suffer, specifically overfilling, which is a technical term in data science related to when the observer reads too much into limited observations. The improved tools consider and search each record one at a time, independent from the rest of the records, trying to determine whether the social media contains the query or not, without paying attention to the entirety of the relevant social media posts and how they apply in different situations. This challenge of modern research tools manifests itself within the produced results.
For other tools, instead of receiving a query, a document is received from the user. Such tools process the uploaded document to extract the main subjects, and then perform a search for these subjects and returns the results. These tools can be treated as a two-step analytical engine: in the first step, the research tool extracts the main subjects of a document with methods such as word frequency, etc.; and in the second step, the research tool performs a regular search for these subjects over the world of associated social media posts. Such research tools suffer from the same problem of overfitting, sensitivity to the details, and lack of a universal measure for assessing relevance in relation to a user's query.
The results of such research tools are sensitive to the query. That is, tweaking the query in a small direction causes the results to change dramatically. The altered query may exist in a different set of case files, and therefore the results are going to be confusingly different. Moreover, since the focus of these research tools is on one document at a time, the struggle is really to combine and sort the results in terms of relevance to the query. Sorting the results is done based on how many common words exist between the query and the case file, or how similar the language of the query is to that of a case. As a result, the results run the risk of being too dependent on the details of the query and the case file, rather than concentrating on the importance of a case and its conceptual relevance to the query.
Power consumption and carbon footprints are other considerations in research systems, and thus should also be addressed. Analytic systems such as the present invention process big data. For example, when a user enters a query to a system, the system takes the query, and searches data that can be composed of tens of millions of files and websites (if not more), to find matches. This single search by itself requires a lot of resources in terms of memory to store the files, compute power to perform the search on a document, and communication to transfer the documents from a hard disk or a memory to the processor for processing. Even for a single search, a regular desktop computer may not perform the task in a timely manner, and therefore a high-performance server is required. Techniques such as database indexing make searching a database faster and more efficient; however, the process of indexing and retrieving information remain a complex, laborious and time-consuming process. As a result, a legal research tool needs a large data center to operate. Such data centers are expensive to purchase, setup, and maintain; they consume a lot of electricity to operate and to cool down; and they have large carbon footprint. It is estimated that data centers consume about 2% of electricity worldwide and that number could rise to 8% by 2030, and much of that electricity is produced from non-renewable sources, contributing to carbon emissions. A research tool can be hosted on a local data center owned by the provider of the research tool, or it can be hosted on the cloud. Either way, the equipment cost, operation cost, and electricity bill will be paid by the provider of the service one way or another. A more efficient social media analysis tool that only needs a small amount of resources, consumes less electricity per query, and has a smaller carbon footprint compared to existing tools such as those discussed above.
Other preexisting technologies do not allow for integration over multiple platforms and require permission and consent from the client to access the data on the post timelines.
As a result, more refined methods of implementing AI and machine learning to address future social media platforms as well as other content.
BRIEF SUMMARY OF THE INVENTIONThe present invention comprises systems and methods analyzing social media content using artificial intelligence/machine learning algorithms. In certain embodiments, the system collects social media data from one or more third-party social media networks associated with the user, where the social media data is comprised of two or more of post reactions, post comments, posting frequency, profile picture, public posting setting, grammar, and predetermined keywords. The system then analyzes, using a machine learning algorithm, the social media data of the user to calculate a social impact score for the user, and transmits the social impact score to the user.
In some embodiments, the social impact score is calculated relative to other social impact scores.
In certain embodiments, the system uses the neural network algorithm to analyze the social media data of the user to identify harmful content.
In yet other embodiments, the system uses the social impact score to correlate a social impact level.
In other embodiments, the machine learning algorithm is comprised of support vector machines (SVM), neural networks, Naïve Bayes classifier, and decision trees.
In some embodiments, the system stores the user's social media data to a user profile.
In other embodiments, the system updates the social impact score in real-time.
In yet other embodiment, the system outputs recommendations on improving the social impact score to the user.
A more complete appreciation of the invention and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein:
In describing a preferred embodiment of the invention illustrated in the drawings, specific terminology will be resorted to for the sake of clarity. However, the invention is not intended to be limited to the specific terms so selected, and it is to be understood that each specific term includes all technical equivalents that operate in a similar manner to accomplish a similar purpose. Several preferred embodiments of the invention are described for illustrative purposes, it being understood that the invention may be embodied in other forms not specifically shown in the drawings.
Since social media posts are created by individuals on individual social media platforms, their posts need to be scanned to determine if they are possibly harmful or not. Post data across multiple platforms is collected and analyzed to determine if a post could be harmful to the client. So, the invention integrates with the social media platforms and pulls posts from the client's timelines, analyzes the posts and notifies the client of possible harmful posts.
Each computer 120 is comprised of a central processing unit 122, a storage medium 124, a user-input device 126, and a display 128. Examples of computers that may be used are: commercially available personal computers, open source computing devices (e.g. Raspberry Pi), commercially available servers, and commercially available portable device (e.g. smartphones, smartwatches, tablets). In one embodiment, each of the peripheral devices 110 and each of the computers 120 of the system may have software related to the system installed on it. In such an embodiment, system data may be stored locally on the networked computers 120 or alternately, on one or more remote servers 140 that are accessible to any of the peripheral devices 110 or the networked computers 120 through a network 130. In alternate embodiments, the software runs as an application on the peripheral devices 110, and include web-based software and iOS-based and Android-based mobile applications.
In order to calculate the score, multiple user parameters from different media have been identified. Each of them has a different assigned weight and threshold as a part of the Social Impact Score calculation. Table 1 lists exemplary parameters and their associated individual scores that are used to calculate the Social Impact Score. The Social Impact Score may be updated in real-time as the user adds to or removes social media content from the Internet.
With reference to the above, in order to calculate the Social Impact Score, multiple user parameters from different media have been identified. Each of them has a different assigned weight and threshold. The combined data of all of them renders the total Score. An exemplary equation for calculating Social Impact Score is provided below, where the Greek characters represent the weight and threshold, which may be adjusted by one of ordinary skill in the art:
Social Impact Score=α*(Reactions)+β*(Comments)+γ*(Posting Frequency)+δ*(Profile Pic)+ε*(Public vs. Private)+ζ*(Grammar/Typos)+η*(KeyWords)
In the table, “SM1” refers to a first social media platform, “SM2” refers to a second social media platform, and “SM3” refers to a third social media platform, where each platform is different.
With regard to the “Reactions” category, the content on each social media platform may be evaluated to identify the average number of reactions the user is getting to their social media posts compared to the total number of followers, which is compared to individual scores cutoffs or ranges a1 through e3 as shown in the table. Then, a numerical value A1 through E3 is calculated. For example, if the average number of reactions is identified for SM1 to be 9-percent, and if a1 is any value greater than 8.6, then A could be assigned a score of 100. Each social media account could use different cutoff; that is a1, a2 and a3 may use different cutoffs or ranges. Likewise, the assigned values A1, A2, and A3 may be different, depending on the relative weighting used for each social media platform.
Table 2 is a diagram showing a possible social impact ranking rubric whereby the social impact score ranking results obtained according to the assigned values in Table 1 are obtained. As shown, the final score is obtained, based on the predetermined categorization by levels. For example, based on the total scores assigned to SM1, SM2, and SM3, the combined scores for all three social media platforms is compared to the table values in Table 2. If the combined score falls in the range V1 to V2, the user's Social Impact Score may be characterized as being “excellent” (or some other characterization used instead of “excellent”). If the combined score falls in the range W1 through W2, the user's Social Impact Score may be characterized as being “very good” (or some other characterization used instead of “very good”), etc.
The foregoing description and drawings should be considered as illustrative only of the principles of the invention. The invention is not intended to be limited by the preferred embodiment and may be implemented in a variety of ways that will be clear to one of ordinary skill in the art. Numerous applications of the invention will readily occur to those skilled in the art. Therefore, it is not desired to limit the invention to the specific examples disclosed or the exact construction and operation shown and described. Rather, all suitable modifications and equivalents may be resorted to, falling within the scope of the invention.
Claims
1. A computer-implemented method comprising:
- collecting social media data from one or more third-party social media networks associated with the user, wherein the social media data is comprised of two or more of post reactions, post comments, posting frequency, profile picture, public posting setting, grammar, and predetermined keywords;
- analyzing, using a machine learning algorithm, the social media data of the user to calculate a social impact score for the user; and
- transmitting the social impact score to the user, wherein the social impact score is calculated relative to other social impact scores.
2. The method of claim 1, further comprising analyzing, using the neural network algorithm, the social media data of the user to identify harmful content.
3. The method of claim 1, wherein the social impact score is correlated to a social impact level.
4. The method of claim 1, wherein the machine learning algorithm is comprised of support vector machines (SVM), neural networks, Naïve Bayes classifier, and decision trees.
5. The method of claim 1, further comprising storing the user's social media data to a user profile.
6. The method of claim 1, further comprising updating the social impact score in real-time.
7. The method of claim 1, further comprising outputting recommendations on improving the social impact score.
8. A computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by one or more processors of a computing device, cause the one or more processors of the computing device to:
- collect social media data from one or more third-party social media networks associated with the user, wherein the social media data is comprised of two or more of post reactions, post comments, posting frequency, profile picture, public posting setting, grammar, and predetermined keywords;
- analyze, using a machine learning algorithm, the social media data of the user to calculate a social impact score for the user; and
- transmit the social impact score to the user, wherein the social impact score is calculated relative to other social impact scores.
9. The computer-readable storage medium of claim 8, wherein the one or more processors analyze, using the neural network algorithm, the social media data of the user to identify harmful content.
10. The computer-readable storage medium of claim 8, wherein the social impact score is correlated to a social impact level.
11. The computer-readable storage medium of claim 8, wherein the machine learning algorithm is comprised of support vector machines (SVM), neural networks, Naïve Bayes classifier, and decision trees.
12. The computer-readable storage medium of claim 8, wherein the one or more processors store the user's social media data to a user profile.
13. The computer-readable storage medium of claim 8, wherein the one or more processors update the social impact score in real-time.
14. The computer-readable storage medium of claim 8, wherein the one or more processors output recommendations on improving the social impact score.
Type: Application
Filed: Feb 24, 2022
Publication Date: Aug 25, 2022
Inventors: Thomas J. Colaiezzi (West Chester, PA), Jemma Barbarise (West Chester, PA), Jan Urban (Prague 2), David Reinberger (Prague 2), Ugur Oruc (Prague 2), Veronika Madzinova (Prague), James Fiala (Guelph)
Application Number: 17/680,198